cryptocurrency macro adoption factors
Description
This research, "Crypto adoption through the lens of institutional and Kirzner’s entrepreneurship theory," investigates the factors driving global cryptocurrency diffusion. Research Hypothesis: The study hypothesized that high formal institutional quality acts as a deterrent to crypto adoption, suggesting that institutional weakness creates a market void that decentralized finance fills. The research aims to explain this pattern by synthesizing Institutional Theory and Kirznerian Entrepreneurship Theory. Data and Methodology: Cross-country Bitcoin adoption scores from the Chainalysis 2024 report were analyzed for 137 nations. The data included over twenty institutional, entrepreneurial, macroeconomic, and demographic variables. A robust Automatic Machine Learning (AML) approach, specifically a Gradient-Boosted Decision Tree (GBDT) model, was used to capture complex, non-linear relationships, validated by OLS and Random Forest models. Notable Findings and Interpretation: The analysis reveals a dual pattern of adoption driven by institutional fragility. The most influential finding is the strong negative correlation between governance stability (a measure of formal institutional quality) and adoption. This is interpreted to mean that the weakening of formal institutions creates an "institutional void" that cryptocurrencies are adopted to fill. Adoption is further enabled by Informal Entrepreneurship and a young median age, which are consistently significant factors. The data shows that adoption thrives in low-trust institutional environments where individuals display entrepreneurial alertness to market inefficiencies and use cryptocurrencies as a necessary hedge against systemic risk (like high inflation or unstable currencies), viewing it as a safer financial alternative. The data can be used to predict future adoption hot spots and informs policymakers that crypto growth is often a symptom of institutional failure, not technological novelty alone
Files
Steps to reproduce
The research gathered secondary, publicly available data for 137 nations, using the Chainalysis 2024 Global Crypto Adoption Index for the dependent variable. Key independent variables—like Governance Stability (institutional factors) and Informal Entrepreneurship—were sourced from institutions like the World Bank's WGI and the IMF. The analysis was executed using an Automatic Machine Learning (AML) workflow on the DataRobot platform, employing a Gradient-Boosted Decision Tree (GBDT) model, which was then validated against Ordinary Least Squares and Random Forest models to ensure the findings' robustness and replicability. The OLS can be reproduced by executing the Jupyter notebook code in the repository
Institutions
- Ariel University Faculty of Social Sciences and Humanities